We actively develop new methodology and explore learning theory relevant to applications in medical imaging. Areas of research include meta-learning, multi-task & continual learning, causality, domain shift, geometric deep learning, semi-supervised and unsupervised learning, representation learning and Bayesian methods.
Causality in Imaging
Causality matters in medical imaging (Nature Communications 2020)
Machine Learning on Graphs
- Graph Convolutional Gaussian Processes (ICML 2019)
- Controlling Meshes via Curvature (IPMI 2019)
- Disease Prediction using Graph Convolutional Networks (MedIA 2018)
- Metric learning with spectral graph convolutions on brain connectivity networks (Neuroimage 2018)
Overfitting & Class-Imbalance
Bayesian Deep Learning & Uncertainty Estimation
Multi-Task & Continual Learning
Towards continual learning in medical imaging (NeurIPS Workshop 2018).